The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.
SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism
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In the citing paper
“Similarly, LightGBM191 has the advantages of faster training speed, higher efficiency, lower memory usage, better accuracy192, and has been consistently outperforming other models193,194.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [194]
Citations
Cites
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
Characterization of Tor Traffic using Time based Features
Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 926
The 1999 DARPA off-line intrusion detection evaluation
Lippmann, R. P., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00139-0 · Cited by 877
On Grouping for Maximum Homogeneity
Fisher, W. D. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501479 · Cited by 796
Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2017) · IEEE Access · doi:10.1109/access.2017.2780250 · Cited by 685
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
Ambusaidi, M. A., He, X., Nanda, P., & Tan, Z. (2016) · IEEE Transactions on Computers · doi:10.1109/tc.2016.2519914 · Cited by 648
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528
Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)
Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G. (2021) · Diagnostics · doi:10.3390/diagnostics11091714 · Cited by 317
Zhang, D., & Gong, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3042848 · Cited by 241
An improved anomaly detection model for IoT security using decision tree and gradient boosting
Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04783-y · Cited by 194
Zero-day attack detection: a systematic literature review
Ahmad, R., Alsmadi, I. M., Al-Hamdani, W. A., & Tawalbeh, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10437-z · Cited by 228
Metadata
- Authors
- Jin, D., Lu, Y., Qin, J., Cheng, Z., & Mao, Z.
- Affiliations
- South China University of Technology (CN)
- Corresponding author
- Jin, Dongzi
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 97
- Pages
- 101984
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3043844898
- Cited by
- 169
- References
- 38
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 30 · 2023: 42 · 2022: 41 · 2021: 22 · 2020: 4
- FWCI
- 12.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Security (publishedVersion)
- Funders
- Guangdong Science and Technology Department
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, real-time attack detection, high-speed networks, LightGBM, categorical variables, time windows, network traffic analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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